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Automatic Modulation Recognition Based on a DCN-BiLSTM Network
Automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956213/ https://www.ncbi.nlm.nih.gov/pubmed/33668245 http://dx.doi.org/10.3390/s21051577 |
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author | Liu, Kai Gao, Wanjun Huang, Qinghua |
author_facet | Liu, Kai Gao, Wanjun Huang, Qinghua |
author_sort | Liu, Kai |
collection | PubMed |
description | Automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the traditional convolutional neural network (CNN) loses the partial phase information of the modulated signal, resulting in low recognition accuracy, we first apply a deep complex network (DCN) to extract the features of the modulated signal containing phase and amplitude information. Then, we cascade bidirectional long short-term memory (BiLSTM) layers to build a bidirectional long short-term memory model according to the extracted features. The BiLSTM layers can extract the contextual information of signals well and address the long-term dependence problems. Next, we feed the features into a fully connected layer. Finally, a softmax classifier is used to perform classification. Simulation experiments show that the performance of our proposed algorithm is better than that of other neural network recognition algorithms. When the signal-to-noise ratio (SNR) exceeds 4 dB, our model’s recognition rate for the 11 modulation signals can reach 90%. |
format | Online Article Text |
id | pubmed-7956213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79562132021-03-15 Automatic Modulation Recognition Based on a DCN-BiLSTM Network Liu, Kai Gao, Wanjun Huang, Qinghua Sensors (Basel) Article Automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the traditional convolutional neural network (CNN) loses the partial phase information of the modulated signal, resulting in low recognition accuracy, we first apply a deep complex network (DCN) to extract the features of the modulated signal containing phase and amplitude information. Then, we cascade bidirectional long short-term memory (BiLSTM) layers to build a bidirectional long short-term memory model according to the extracted features. The BiLSTM layers can extract the contextual information of signals well and address the long-term dependence problems. Next, we feed the features into a fully connected layer. Finally, a softmax classifier is used to perform classification. Simulation experiments show that the performance of our proposed algorithm is better than that of other neural network recognition algorithms. When the signal-to-noise ratio (SNR) exceeds 4 dB, our model’s recognition rate for the 11 modulation signals can reach 90%. MDPI 2021-02-24 /pmc/articles/PMC7956213/ /pubmed/33668245 http://dx.doi.org/10.3390/s21051577 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Kai Gao, Wanjun Huang, Qinghua Automatic Modulation Recognition Based on a DCN-BiLSTM Network |
title | Automatic Modulation Recognition Based on a DCN-BiLSTM Network |
title_full | Automatic Modulation Recognition Based on a DCN-BiLSTM Network |
title_fullStr | Automatic Modulation Recognition Based on a DCN-BiLSTM Network |
title_full_unstemmed | Automatic Modulation Recognition Based on a DCN-BiLSTM Network |
title_short | Automatic Modulation Recognition Based on a DCN-BiLSTM Network |
title_sort | automatic modulation recognition based on a dcn-bilstm network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956213/ https://www.ncbi.nlm.nih.gov/pubmed/33668245 http://dx.doi.org/10.3390/s21051577 |
work_keys_str_mv | AT liukai automaticmodulationrecognitionbasedonadcnbilstmnetwork AT gaowanjun automaticmodulationrecognitionbasedonadcnbilstmnetwork AT huangqinghua automaticmodulationrecognitionbasedonadcnbilstmnetwork |